Maintaining timestamp parity of objects with alternate data streams during transition phase to synchronous state

ABSTRACT

Techniques are provided for maintaining timestamp parity during a transition replay phase to a synchronous state. During a transition logging phase where metadata operations executed by a primary node are logged into a metadata log and regions modified by data operations executed by the primary node are tracked within a dirty region log, a close stream operation to close a stream associated with a basefile of the primary node is identified. A determination is made as to whether the dirty region log comprises an entry for the stream indicating that a write data operation previously modified the stream. In an example, in response to the dirty region log comprising the entry, an indicator is set to specify that the stream was deleted by the close stream operation. In another example, a modify timestamp of the basefile is logged into the metadata log for subsequent replication to the secondary node.

BACKGROUND

Many storage systems may implement data replication and/or otherredundancy data access techniques for data loss protection andnon-disruptive client access. For example, a first computing device maybe configured to provide clients with primary access to data storedwithin a first storage device and/or other storage devices. A secondcomputing device may be configured as a backup for the first computingdevice in the event the first computing device fails. Data may bereplicated from the first computing device to the second computingdevice. In this way, the second computing device can provide clientswith access to replicated data in the event the first computing devicefails.

One type of replication is asynchronous replication. When the firstcomputing device receives an operation from a client device, the firstcomputing device transmits a replication of the operation to the secondcomputing device for execution. Irrespective of whether the secondcomputing device has executed the replicated operation, the firstcomputing device will transmit an acknowledgment of successfulperformance of the operation to the client device once the firstcomputing device has executed the operation.

Another type of replication is synchronous replication, which provides agreater level of data protection guarantees. This is because the firstcomputing device does not transmit the acknowledgment until theoperation has been executed by the first computing device and thereplication operation has been executed or acknowledged by the secondcomputing device. In this way, two copies of data and/or metadataresulting from the operation are maintained before the client receivesacknowledgment that the operation was successful.

Unfortunately, the first computing device and the second computingdevice can fall out of sync due to network transmission errors, computerfailures, and/or other issues that will cause data maintained by thefirst computing device to diverge from replicated data maintained by thesecond computing device. Thus, the data protection guarantees providedby synchronous replication cannot be provided until storage of the firstcomputing device and the second computing device are brought back into asynchronous replication state. Current resynchronization processes canbe very disruptive to clients because client operations will be quiesced(e.g., client I/O operations will be blocked, failed, stopped, or queuedfor later execution) during various phases of resynchronization.Blocking client I/O can cause applications to time out, experienceerrors, increase client experienced latency, and disrupt access to data.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example computing environmentin which an embodiment of the invention may be implemented.

FIG. 2 is a block diagram illustrating a network environment withexemplary node computing devices.

FIG. 3 is a block diagram illustrating an exemplary node computingdevice.

FIG. 4 is a flow chart illustrating an example method for maintainingtimestamp parity during a transition replay phase to a synchronous stateby utilizing an indicator.

FIG. 5A is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by utilizing an indicator.

FIG. 5B is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by utilizing an indicator, where a write dataoperation is tracked using a dirty region log.

FIG. 5C is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by utilizing an indicator, where a close streamoperation is tracked using a metadata log.

FIG. 5D is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by utilizing an indicator, where a metadata log replayphase of the transition replay phase is performed.

FIG. 5E is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by utilizing an indicator, where a dirty region logscan phase of the transition replay phase is performed.

FIG. 5F is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state utilizing an indicator, where a dirty region log scanis failed and a resynchronization is triggered based upon an indicator.

FIG. 5G is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by utilizing an indicator, where a resynchronizationis performed.

FIG. 6 is a flow chart illustrating an example method for maintainingtimestamp parity during a transition replay phase to a synchronous stateby logging a modify timestamp and a change timestamp within a metadatalog.

FIG. 7A is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by logging a modify timestamp and a change timestampwithin a metadata log.

FIG. 7B is a block diagram illustrating an example system formaintaining timestamp parity during a transition replay phase to asynchronous state by replaying and applying a close stream operation, amodify timestamp, and a change timestamp from a metadata log to asecondary node.

FIG. 8 is an example of a computer readable medium in which anembodiment of the invention may be implemented.

DETAILED DESCRIPTION

Some examples of the claimed subject matter are now described withreference to the drawings, where like reference numerals are generallyused to refer to like elements throughout. In the following description,for purposes of explanation, numerous specific details are set forth inorder to provide an understanding of the claimed subject matter. It maybe evident, however, that the claimed subject matter may be practicedwithout these specific details. Nothing in this detailed description isadmitted as prior art.

In asynchronous replication, incremental changes to a storage object,such as a volume, a file, a directory, a defined set of files ordirectories, a file system, a basefile, a stream, or a storage virtualmachine hosting one or more volumes stored across one or more nodes of acluster, are replicated from the storage object to a replicated storageobject. In synchronous replication, when an operation is received from aclient device (e.g., a write operation targeting the storage object),the operation is split to create a replicated operation that is areplica of the operation. The operation is executed upon the storageobject, such as by a primary node managing the storage object. Thereplicated operation is executed upon the replicated storage object,such as by a secondary node managing the replicated storage object. Theoperation is not acknowledged to the client device as being completeuntil both the operation and the replicated operation have successfullybeen executed upon the storage object and the replicated storage object.Synchronous replication and/or asynchronous replication may beimplemented by various types of nodes, such as computing devices,servers, virtual machines, hardware, software, computing resourceswithin a cloud computing environment, and/or combinations thereof.

Synchronous replication can be implemented for a new storage object,such as a new volume, in a relatively easy manner. This is because thereare no pending client I/O, making real-time changes and modifications tothe new volume, which need to be dealt with in order to make areplicated volume of the new volume consistent with the new volume.These pending I/O would otherwise need to be handled so that the newvolume and the replicated volume have the same data as a baseline forstarting to synchronously replicate new incoming client I/O.

However, for an existing volume that already comprises data that isbeing actively modified in real-time (e.g., by incoming client I/O), areplicated volume will have to be brought into sync with respect to theexisting volume so that the replicated volume has the same data as theexisting volume. Because the existing volume is used to actively processclient I/O, the replicated volume will lag behind the existing volumedue to the client I/O modifying the existing volume. Thus, conventionaltechniques for transitioning to synchronous replication (e.g.,transitioning from a non-synchronous replication state such as an out ofsync state or an asynchronous replication state to a synchronousreplication state) must pause client I/O (e.g., stop, block, fail, orqueue the client I/O for later execution), which increases latency(e.g., increased latency while the client I/O is queued). This alsoaffects the operation of client devices accessing data within theexisting volume (e.g., an application may timeout or experience errorswhen data access operations are blocked or failed).

Accordingly, a transition logging phase and a transition replay phasemay be performed to transition a storage object and a replicated storageobject from an asynchronous replication state to a synchronousreplication state (e.g., an in-sync state) in a manner that mitigatesclient disruption and latency. In particular, the transition loggingphase and the transition replay phase can be performed without holdingclient I/O (e.g., without pausing, blocking, failing, or queueing forlater execution), which reduces client latency that would otherwise beexperienced if the client I/O was held during the transition.

During the transition logging phase, a dirty region log is used to trackregions within the storage object that are modified by data operations,such as write operations executed during a last asynchronous incrementaltransfer (e.g., asynchronous incremental transfers may be initiallyperformed to incrementally transfer data from the storage object to areplicated storage object to help make the replicated storage objectcomprise more of the same data as the storage object). The dirty regionlog may comprise bits that can be set to either a dirty indicator or aclean indicator. A bit may be mapped to a region within the storageobject. Thus, the bit can be set to the dirty indicator to indicate thata data operation has modified the region (e.g., the region now comprisesdata not yet replicated to the replicated storage object). The bit canbe set to the clean indicator to indicate that the region is clean(e.g., the region does not comprise data not yet replicated to thereplicated storage object, and thus the region comprises the same dataas a corresponding region within the replicated storage object).

In an embodiment, dirty region logs are created as incore dirty regionlogs (e.g., maintained in memory) for each storage object of aconsistency group, such as for each file of the consistency group. Also,incore splitter objects (e.g., functionality configured to intercept andreplicate operations) are set up for each replication endpoint (e.g.,the replicated storage object hosted by the secondary node) and are setto a dirty region logging state. This ensures that incoming clientwrites are intercepted by the splitter objects, and for each region thatis modified by the incoming client writes, dirty bits are set in thedirty region log. Thus, regions that are dirty are captured incoreduring the transition logging phase utilizing the dirty region log.

During the transition logging phase, a metadata log is used to trackmetadata operations executed by the primary node hosting the storageobject, such as a create operation (e.g., a create file operation, acreate LUN operation, a create basefile operation, a create streamoperation, etc.), a link operation, an unlink operation, a renameoperation (e.g., a file rename operation, etc.), a set attributeoperation (e.g., a set volume size operation, an assign permissionsoperation, etc.), a close operation (e.g., a close stream operation thatcloses and deletes a stream associated with a basefile), etc. Inparticular, copies of metadata operations executed upon the storageobject during the last asynchronous transfer are inserted into themetadata log. In an embodiment of tracking metadata operations, themetadata operations are assigned sequence numbers based upon the orderthat the metadata operations were executed upon the storage object bythe primary node. The metadata operations are inserted into the metadatalog with the sequence numbers. In an embodiment, the metadata operationsare sorted within the metadata log based upon the sequence numbers orare inserted into the metadata log based upon the sequence numbers. Ifexecution of a metadata operation modified a timestamp, such as a changetimestamp of a basefile modified by a metadata operation directed to astream associated with the basefile, then the timestamp is recorded intothe metadata log for replication to the secondary node. In anembodiment, merely a single timestamp is recorded within the metadatalog for each metadata operation. For example, an operation handler picksup a system timestamp. Logic of the operation handler determines if achange timestamp (ctime) alone or a modify timestamp and changetimestamp <mtime, ctime> have to be set to the system timestamp. Asynchronous replication component may copy out a timestamp into themetadata log.

Once the last asynchronous transfer is complete, then a transitionreplay phase is performed to transition the storage object and thereplicated storage object to an in-sync state (e.g., transition from anon-synchronous replication state such as an out of sync state or anasynchronous replication state to a synchronous replication state). Thetransition replay phase comprises a metadata log scan/metadata logreplay phase followed by a dirty region log scan/dirty region log replayphase. In an embodiment, a cutover scanner performs the transitionreplay phase. The cutover scanner may be implemented at the primary nodeor at any other device or node. During a metadata log scan/replay phaseof the transition replay phase, the metadata operations within themetadata log are replicated to the replicated storage object accordingto an order that the metadata operations were executed upon the storageobject in order to maintain consistency. During replay, a timestamp of ametadata operation being replayed from the metadata log is passed to thesecondary node. Logic of the operation handler at the secondary nodedetermines if both the modify timestamp and/or the change timestamp hasto be modified to a value of the timestamp passed to the secondary nodeby the primary node.

After the metadata operations are replicated to the secondary node forexecution upon the replicated storage object according to the sequencenumbers, the dirty regions identified by the dirty region log (e.g.,regions having corresponding dirty indicators set for the regions withinthe dirty region log) are replicated from the storage object to thereplicated storage object during a dirty region log scan phase of thetransition replay phase. That is, the data within the dirty regions(e.g., “dirty” data not yet replicated to the replicated storage object)is transmitted to the secondary node for storage into correspondingregions within the replicated storage object. The replication of thedirty region is triggered based upon completion of the replication ofthe metadata operations. That is, metadata operations may be replayedbefore data of dirty regions is replicated to the secondary node.

During a dirty region log scan phase (sub-phase) of the transitionreplay phase, the splitter objects are changed to a cut over splitstate. From this point forward during the dirty region log scan phase,for every incoming client I/O, a corresponding dirty region log for atarget storage object is evaluated. If a write operation targets a dirtyregion of a storage object that is not locked (e.g., the dirty regionmay be locked from being modified when data of that dirty region isbeing replicated by the cutover scanner), then the write is executedupon the storage object. If a write operation targets a non-dirty orpartially dirty region, then data of the write operation is written tothe storage object and is split/replicated to a replicated storageobject (e.g., synchronously replicated to the secondary node).

The cutover scanner may also be executed to read the incore dirty regionlogs during a dirty region log scan phase. For every dirty regionidentified, dirty data is replicated to the replicated storage object.During the replication, a lock can be obtained for the dirty region sothat any writes to the dirty region are blocked while data of thatparticular dirty region is being replicated by the cutover scannerduring the dirty region log scan phase. The lock is removed once thesecondary node writes the replicated dirty data to the replicatedstorage object. During the dirty region log scan, the modify timestampand change timestamp are explicitly replicated.

In various situations such as where a write to an NT stream is followedby deletion of the NT stream during the transition logging phase,timestamp mismatch can occur between the primary node and the secondarynode after the transition replay phase to an in-sync state, such as to asynchronous replication state between the primary node and the secondarynode. This can lead to inconsistencies and other operational issues,such as after a switchover from the primary node to the secondary nodeoccurs in response to the primary node failing. After the switchover,the secondary node is actively providing clients with access toreplicated data that could have inconsistent timestamps in relation totimestamps maintained by the failed primary node. In an example of howtimestamp mismatch may occur, a primary node may maintain a basefile.The basefile represents main content of a file, such as a CommonInternet File System (CIFS) file. One or more streams (e.g., an NTstream) may be associated with the basefile. In an example, a stream maycorrespond to additional data associate with the basefile. The basefilemay have a modify timestamp (mtime) and a change timestamp (ctime). Themodify timestamp may correspond to a last time to which the basefile waswritten. The change timestamp may correspond to a time at which an inodeof the basefile was last modified.

Initially, the modify timestamp and the change timestamp of the basefilemaintained by the primary node may be set to t0. During the transitionlogging phase, a write data operation may be received by the primarynode. The write data operation may be directed to a stream (e.g., an NTstream) of the basefile. The write data operation may be executed at t1by the primary node, and thus the modify timestamp is set to t1 and thechange timestamp is set to t1. Execution of the write data operation maybe tracked using the dirty region log. In particular, a region ofstorage maintained by the primary node may be marked as dirty byassociating a dirty indicator with the region through the dirty regionlog.

Subsequently, a close stream operation may be received by the primarynode. The close stream operation may be a metadata operation whoseexecution is logged within the metadata log. The close stream operationmay be executed at t2 by the primary node. Execution of the close streamoperation will close the stream of the basefile. If the stream is an NTstream, then the close stream operation will delete the NT stream,whereas close operations for other types of objects (other types ofstreams) may not delete the stream being closed. Execution of the closestream operation at t2 will result in the change timestamp of thebasefile being set to t2. However, the modify timestamp will remain att1 because execution of the close stream operation does not affect themodify timestamp, and thus only the change timestamp of t2 will belogged within the metadata log. In an example, the modify timestampremains at t1 as a result of behavior of the basefile being a CIFS file.In general, any modification to an NT stream results in the modificationof timestamps of the basefile since the NT stream is considered anextension of the basefile itself. In the case of an NT stream beingdeleted by a close stream operation, only the change timestamp of thebasefile changes as per the specification of the CIFS protocol. Thus,the modify timestamp, which is left unchanged by execution of the closestream operation, is associated with the basefile. After execution ofthe write data operation and the close stream operation, the modifytimestamp is t1 and the change timestamp is t2.

During the metadata log replay phase of the transition replay phase, theclose stream operation is replayed upon a replicated basefile that ismaintained by the secondary node as a replica of the basefile maintainedby the primary node. In this way, a change timestamp of the replicatedbasefile will be set to t2 (e.g., the t2 value may be read from themetadata log and applied to the change timestamp of the replicatedbasefile). However, the modify timestamp of the replicated basefile willremain at t0 because only the change timestamp was recorded within themetadata log since execution of the close stream operation by theprimary node merely affected the change timestamp of the basefilemaintained by the primary node. This is because the operation is on thestream, but the timestamps are changed on the basefile. Because theremay not be a dirty region log for the basefile (e.g., because nothingchanged in the basefile data contents), there is no opportunity to fixthe basefile data contents during the dirty region log scan phase.

After the metadata log replay phase completes, a dirty region log scanphase is performed to replicate dirty data (e.g., data of the basefilethat has been modified during the transition logging phase, which hasyet to be replicated to the replicated basefile) from the basefile tothe replicated basefile. When a dirty region log scan attempts to readthe content of the stream (e.g., the stream may be associated with adirty region modified by the write data operation) that no longer existsbecause the close stream operation deleted the stream, the dirty regionlog induced read will fail. The failure is expected, and thus the dirtyregion log scan continues to the next storage object. Unfortunately,this leaves the modify timestamp of the replica basefile at t0 insteadof t1. Thus, there is a mismatch between the modify timestamp of t1 forthe basefile at the primary node and the modify timestamp of t0 for thereplica basefile at the secondary node.

Accordingly, as provided herein, timestamp parity may be maintainedduring the transition replay phase to an in-sync state, such as to asynchronous replication state where incoming operations to the primarynode are synchronously replicated to the secondary node. In anembodiment of maintaining timestamp consistency, an indicator, such as aflag, is utilized for timestamp parity. For example, a transitionlogging phase is performed where metadata operations executed by theprimary node are logged into a metadata log and regions modified by dataoperations executed by the primary node are tracked within a dirtyregion log. During the transition logging phase, a close streamoperation to close a stream associated with a basefile of the primarynode may be identified.

In response to identifying the close stream operation, the dirty regionlog is evaluated to determine whether any write data operations modifiedthe stream of the basefile before the close stream operation closes anddeletes the stream. In particular, the dirty region log is evaluated todetermine whether the dirty region log comprises an entry for the streamindicating that a write data operation modified the stream (e.g.,modified a region of storage associated with the stream) before theclose stream operation is executed by the primary node. If the entryexists within the dirty region log, then a determination is made that aprior write data operation modified a change timestamp and a modifytimestamp of the basefile associated with the stream. Because the closestream operation deletes the stream and only modifies the changetimestamp, only the change timestamp is recorded within the metadata logand a value of the modify timestamp set by the prior write dataoperation will not be replicated during a subsequent transition replayphase where a dirty region log scan is performed. Accordingly, anindicator, such as a flag, bit, or other indicator (referred togenerally herein as an “indicator”), is set to specify that the streamwas deleted by the close stream operation.

During the subsequent transition replay phase where a dirty region logscan phase is being performed to replicate the dirty data identified bythe dirty region log from the primary node to the secondary node, theindicator for the stream will be encountered. Encountering the indicatorwill cause the transition to fail and will trigger a restart of theresynchronization. The resynchronization will replicate the modifytimestamp of the basefile from the primary node to a replicated basefilemaintained by the secondary node as a replica of the basefile. In thisway, the modify timestamp of the basefile and a modified timestamp ofthe replicated basefile will have the same value instead of differentvalues.

In another embodiment of maintaining timestamp consistency, the modifytimestamp of the basefile may be recorded into the metadata log duringthe transition logging phase in response to the primary node executingthe close stream operation to close and delete the stream of thebasefile (e.g., as opposed to utilizing an indicator to specify that theclose stream operation closed the stream that was previously written toby the write data operation). Normally, merely the change timestamp ofthe basefile would be recorded into the metadata log in response to theclose stream operation being executed and logged within the metadatalog, because execution of the close stream operation does not change themodify timestamp of the basefile. During replay of the metadata logduring the metadata log replay phase of the transition replay phase, theclose stream operation, the change timestamp, and the modify timestampwithin the metadata log are transmitted to the secondary node to applyto the replicated basefile. In this way, the modify timestamp of thebasefile and the modify timestamp of the replicated basefile will havethe same value instead of different values because the modify timestampof the basefile is logged within the metadata log in response to theclose stream operation being executed, and the modify timestamp is thenapplied to the secondary node.

The ability to maintain timestamp parity/consistency between the primarynode and secondary node so that a non-disruptive transition from anasynchronous replication state to an in-sync state such as a synchronousreplication state will mitigate inconsistencies, such as timestampinconsistencies, between the primary node and the secondary node. Theability to non-disruptively transition to the synchronous replicationstate without introducing timestamp inconsistencies provides improveddata protection guarantees, such as zero recovery point objective (RPO)support for network attached storage (NAS), common internet file system(CIFS), and NT streams.

FIG. 1 is a diagram illustrating an example operating environment 100 inwhich an embodiment of the techniques described herein may beimplemented. In one example, the techniques described herein may beimplemented within a client device 128, such as a laptop, a tablet, apersonal computer, a mobile device, a server, a virtual machine, awearable device, etc. In another example, the techniques describedherein may be implemented within one or more nodes, such as a first node130 and/or a second node 132 within a first cluster 134, a third node136 within a second cluster 138, etc. A node may comprise a storagecontroller, a server, an on-premise device, a virtual machine such as astorage virtual machine, hardware, software, or combination thereof. Theone or more nodes may be configured to manage the storage and access todata on behalf of the client device 128 and/or other client devices. Inanother example, the techniques described herein may be implementedwithin a distributed computing platform 102 such as a cloud computingenvironment (e.g., a cloud storage environment, a multi-tenant platform,a hyperscale infrastructure comprising scalable server architectures andvirtual networking, etc.) configured to manage the storage and access todata on behalf of client devices and/or nodes.

In yet another example, at least some of the techniques described hereinare implemented across one or more of the client device 128, the one ormore nodes 130, 132, and/or 136, and/or the distributed computingplatform 102. For example, the client device 128 may transmitoperations, such as data operations to read data and write data andmetadata operations (e.g., a create file operation, a rename directoryoperation, a resize operation, a set attribute operation, etc.), over anetwork 126 to the first node 130 for implementation by the first node130 upon storage. The first node 130 may store data associated with theoperations within volumes or other data objects/structures hosted withinlocally attached storage, remote storage hosted by other computingdevices accessible over the network 126, storage provided by thedistributed computing platform 102, etc. The first node 130 mayreplicate the data and/or the operations to other computing devices,such as to the second node 132, the third node 136, a storage virtualmachine executing within the distributed computing platform 102, etc.,so that one or more replicas of the data are maintained. For example,the third node 136 may host a destination storage volume that ismaintained as a replica of a source storage volume of the first node130. Such replicas can be used for disaster recovery and failover.

In an embodiment, the techniques described herein are implemented by astorage operating system or are implemented by a separate module thatinteracts with the storage operating system. The storage operatingsystem may be hosted by the client device, 128, a node, the distributedcomputing platform 102, or across a combination thereof. In an example,the storage operating system may execute within a storage virtualmachine, a hyperscaler, or other computing environment. The storageoperating system may implement a storage file system to logicallyorganize data within storage devices as one or more storage objects andprovide a logical/virtual representation of how the storage objects areorganized on the storage devices. A storage object may comprise anylogically definable storage element stored by the storage operatingsystem (e.g., a volume stored by the first node 130, a cloud objectstored by the distributed computing platform 102, etc.). Each storageobject may be associated with a unique identifier that uniquelyidentifies the storage object. For example, a volume may be associatedwith a volume identifier uniquely identifying that volume from othervolumes. The storage operating system also manages client access to thestorage objects.

The storage operating system may implement a file system for logicallyorganizing data. For example, the storage operating system may implementa write anywhere file layout for a volume where modified data for a filemay be written to any available location as opposed to a write-in-placearchitecture where modified data is written to the original location,thereby overwriting the previous data. In an example, the file systemmay be implemented through a file system layer that stores data of thestorage objects in an on-disk format representation that is block-based(e.g., data is stored within 4 kilobyte blocks and inodes are used toidentify files and file attributes such as creation time, accesspermissions, size and block location, etc.).

In an example, deduplication may be implemented by a deduplicationmodule associated with the storage operating system. Deduplication isperformed to improve storage efficiency. One type of deduplication isinline deduplication that ensures blocks are deduplicated before beingwritten to a storage device. Inline deduplication uses a data structure,such as an incore hash store, which maps fingerprints of data to datablocks of the storage device storing the data. Whenever data is to bewritten to the storage device, a fingerprint of that data is calculatedand the data structure is looked up using the fingerprint to findduplicates (e.g., potentially duplicate data already stored within thestorage device). If duplicate data is found, then the duplicate data isloaded from the storage device and a byte by byte comparison may beperformed to ensure that the duplicate data is an actual duplicate ofthe data to be written to the storage device. If the data to be writtenis a duplicate of the loaded duplicate data, then the data to be writtento disk is not redundantly stored to the storage device. Instead, apointer or other reference is stored in the storage device in place ofthe data to be written to the storage device. The pointer points to theduplicate data already stored in the storage device. A reference countfor the data may be incremented to indicate that the pointer nowreferences the data. If at some point the pointer no longer referencesthe data (e.g., the deduplicated data is deleted and thus no longerreferences the data in the storage device), then the reference count isdecremented. In this way, inline deduplication is able to deduplicatedata before the data is written to disk. This improves the storageefficiency of the storage device.

Background deduplication is another type of deduplication thatdeduplicates data already written to a storage device. Various types ofbackground deduplication may be implemented. In an example of backgrounddeduplication, data blocks that are duplicated between files arerearranged within storage units such that one copy of the data occupiesphysical storage. References to the single copy can be inserted into afile system structure such that all files or containers that contain thedata refer to the same instance of the data. Deduplication can beperformed on a data storage device block basis. In an example, datablocks on a storage device can be identified using a physical volumeblock number. The physical volume block number uniquely identifies aparticular block on the storage device. Additionally, blocks within afile can be identified by a file block number. The file block number isa logical block number that indicates the logical position of a blockwithin a file relative to other blocks in the file. For example, fileblock number 0 represents the first block of a file, file block number 1represents the second block, etc. File block numbers can be mapped to aphysical volume block number that is the actual data block on thestorage device. During deduplication operations, blocks in a file thatcontain the same data are deduplicated by mapping the file block numberfor the block to the same physical volume block number, and maintaininga reference count of the number of file block numbers that map to thephysical volume block number. For example, assume that file block number0 and file block number 5 of a file contain the same data, while fileblock numbers 1-4 contain unique data. File block numbers 1-4 are mappedto different physical volume block numbers. File block number 0 and fileblock number 5 may be mapped to the same physical volume block number,thereby reducing storage requirements for the file. Similarly, blocks indifferent files that contain the same data can be mapped to the samephysical volume block number. For example, if file block number 0 offile A contains the same data as file block number 3 of file B, fileblock number 0 of file A may be mapped to the same physical volume blocknumber as file block number 3 of file B.

In another example of background deduplication, a changelog is utilizedto track blocks that are written to the storage device. Backgrounddeduplication also maintains a fingerprint database (e.g., a flatmetafile) that tracks all unique block data such as by tracking afingerprint and other filesystem metadata associated with block data.Background deduplication can be periodically executed or triggered basedupon an event such as when the changelog fills beyond a threshold. Aspart of background deduplication, data in both the changelog and thefingerprint database is sorted based upon fingerprints. This ensuresthat all duplicates are sorted next to each other. The duplicates aremoved to a dup file. The unique changelog entries are moved to thefingerprint database, which will serve as duplicate data for a nextdeduplication operation. In order to optimize certain filesystemoperations needed to deduplicate a block, duplicate records in the dupfile are sorted in certain filesystem sematic order (e.g., inode numberand block number). Next, the duplicate data is loaded from the storagedevice and a whole block byte by byte comparison is performed to makesure duplicate data is an actual duplicate of the data to be written tothe storage device. After, the block in the changelog is modified topoint directly to the duplicate data as opposed to redundantly storingdata of the block.

In an example, deduplication operations performed by a datadeduplication layer of a node can be leveraged for use on another nodeduring data replication operations. For example, the first node 130 mayperform deduplication operations to provide for storage efficiency withrespect to data stored on a storage volume. The benefit of thededuplication operations performed on first node 130 can be provided tothe second node 132 with respect to the data on first node 130 that isreplicated to the second node 132. In some aspects, a data transferprotocol, referred to as the LRSE (Logical Replication for StorageEfficiency) protocol, can be used as part of replicating consistencygroup differences from the first node 130 to the second node 132. In theLRSE protocol, the second node 132 maintains a history buffer that keepstrack of data blocks that it has previously received. The history buffertracks the physical volume block numbers and file block numbersassociated with the data blocks that have been transferred from firstnode 130 to the second node 132. A request can be made of the first node130 to not transfer blocks that have already been transferred. Thus, thesecond node 132 can receive deduplicated data from the first node 130,and will not need to perform deduplication operations on thededuplicated data replicated from first node 130.

In an example, the first node 130 may preserve deduplication of datathat is transmitted from first node 130 to the distributed computingplatform 102. For example, the first node 130 may create an objectcomprising deduplicated data. The object is transmitted from the firstnode 130 to the distributed computing platform 102 for storage. In thisway, the object within the distributed computing platform 102 maintainsthe data in a deduplicated state. Furthermore, deduplication may bepreserved when deduplicated data is transmitted/replicated/mirroredbetween the client device 128, the first node 130, the distributedcomputing platform 102, and/or other nodes or devices.

In an example, compression may be implemented by a compression moduleassociated with the storage operating system. The compression module mayutilize various types of compression techniques to replace longersequences of data (e.g., frequently occurring and/or redundantsequences) with shorter sequences, such as by using Huffman coding,arithmetic coding, compression dictionaries, etc. For example, anuncompressed portion of a file may comprise “ggggnnnnnnqqqqqqqqqq”,which is compressed to become “4g6n10q”. In this way, the size of thefile can be reduced to improve storage efficiency. Compression may beimplemented for compression groups. A compression group may correspondto a compressed group of blocks. The compression group may berepresented by virtual volume block numbers. The compression group maycomprise contiguous or non-contiguous blocks.

Compression may be preserved when compressed data istransmitted/replicated/mirrored between the client device 128, a node,the distributed computing platform 102, and/or other nodes or devices.For example, an object may be created by the first node 130 to comprisecompressed data. The object is transmitted from the first node 130 tothe distributed computing platform 102 for storage. In this way, theobject within the distributed computing platform 102 maintains the datain a compressed state.

In an example, various types of synchronization may be implemented by asynchronization module associated with the storage operating system. Inan example, synchronous replication may be implemented, such as betweenthe first node 130 and the second node 132. It may be appreciated thatthe synchronization module may implement synchronous replication betweenany devices within the operating environment 100, such as between thefirst node 130 of the first cluster 134 and the third node 136 of thesecond cluster 138 and/or between a node of a cluster and an instance ofa node or virtual machine in the distributed computing platform 102.

As an example, during synchronous replication, the first node 130 mayreceive a write operation from the client device 128. The writeoperation may target a file stored within a volume managed by the firstnode 130. The first node 130 replicates the write operation to create areplicated write operation. The first node 130 locally implements thewrite operation upon the file within the volume. The first node 130 alsotransmits the replicated write operation to a synchronous replicationtarget, such as the second node 132 that maintains a replica volume as areplica of the volume maintained by the first node 130. The second node132 will execute the replicated write operation upon the replica volumeso that file within the volume and the replica volume comprises the samedata. After, the second node 132 will transmit a success message to thefirst node 130. With synchronous replication, the first node 130 doesnot respond with a success message to the client device 128 for thewrite operation until both the write operation is executed upon thevolume and the first node 130 receives the success message that thesecond node 132 executed the replicated write operation upon the replicavolume.

In another example, asynchronous replication may be implemented, such asbetween the first node 130 and the third node 136. It may be appreciatedthat the synchronization module may implement asynchronous replicationbetween any devices within the operating environment 100, such asbetween the first node 130 of the first cluster 134 and the distributedcomputing platform 102. In an example, the first node 130 may establishan asynchronous replication relationship with the third node 136. Thefirst node 130 may capture a baseline snapshot of a first volume as apoint in time representation of the first volume. The first node 130 mayutilize the baseline snapshot to perform a baseline transfer of the datawithin the first volume to the third node 136 in order to create asecond volume within the third node 136 comprising data of the firstvolume as of the point in time at which the baseline snapshot wascreated.

After the baseline transfer, the first node 130 may subsequently createsnapshots of the first volume over time. As part of asynchronousreplication, an incremental transfer is performed between the firstvolume and the second volume. In particular, a snapshot of the firstvolume is created. The snapshot is compared with a prior snapshot thatwas previously used to perform the last asynchronous transfer (e.g., thebaseline transfer or a prior incremental transfer) of data to identify adifference in data of the first volume between the snapshot and theprior snapshot (e.g., changes to the first volume since the lastasynchronous transfer). Accordingly, the difference in data isincrementally transferred from the first volume to the second volume. Inthis way, the second volume will comprise the same data as the firstvolume as of the point in time when the snapshot was created forperforming the incremental transfer. It may be appreciated that othertypes of replication may be implemented, such as semi-sync replication.

In an embodiment, the first node 130 may store data or a portion thereofwithin storage hosted by the distributed computing platform 102 bytransmitting the data within objects to the distributed computingplatform 102. In one example, the first node 130 may locally storefrequently accessed data within locally attached storage. Lessfrequently accessed data may be transmitted to the distributed computingplatform 102 for storage within a data storage tier 108. The datastorage tier 108 may store data within a service data store 120, and maystore client specific data within client data stores assigned to suchclients such as a client (1) data store 122 used to store data of aclient (1) and a client (N) data store 124 used to store data of aclient (N). The data stores may be physical storage devices or may bedefined as logical storage, such as a virtual volume, LUNs, or otherlogical organizations of data that can be defined across one or morephysical storage devices. In another example, the first node 130transmits and stores all client data to the distributed computingplatform 102. In yet another example, the client device 128 transmitsand stores the data directly to the distributed computing platform 102without the use of the first node 130.

The management of storage and access to data can be performed by one ormore storage virtual machines (SVMs) or other storage applications thatprovide software as a service (SaaS) such as storage software services.In one example, an SVM may be hosted within the client device 128,within the first node 130, or within the distributed computing platform102 such as by the application server tier 106. In another example, oneor more SVMs may be hosted across one or more of the client device 128,the first node 130, and the distributed computing platform 102. The oneor more SVMs may host instances of the storage operating system.

In an example, the storage operating system may be implemented for thedistributed computing platform 102. The storage operating system mayallow client devices to access data stored within the distributedcomputing platform 102 using various types of protocols, such as aNetwork File System (NFS) protocol, a Server Message Block (SMB)protocol and Common Internet File System (CIFS), and Internet SmallComputer Systems Interface (iSCSI), and/or other protocols. The storageoperating system may provide various storage services, such as disasterrecovery (e.g., the ability to non-disruptively transition clientdevices from accessing a primary node that has failed to a secondarynode that is taking over for the failed primary node), backup andarchive function, replication such as asynchronous and/or synchronousreplication, deduplication, compression, high availability storage,cloning functionality (e.g., the ability to clone a volume, such as aspace efficient flex clone), snapshot functionality (e.g., the abilityto create snapshots and restore data from snapshots), data tiering(e.g., migrating infrequently accessed data to slower/cheaper storage),encryption, managing storage across various platforms such as betweenon-premise storage systems and multiple cloud systems, etc.

In one example of the distributed computing platform 102, one or moreSVMs may be hosted by the application server tier 106. For example, aserver (1) 116 is configured to host SVMs used to execute applicationssuch as storage applications that manage the storage of data of theclient (1) within the client (1) data store 122. Thus, an SVM executingon the server (1) 116 may receive data and/or operations from the clientdevice 128 and/or the first node 130 over the network 126. The SVMexecutes a storage application and/or an instance of the storageoperating system to process the operations and/or store the data withinthe client (1) data store 122. The SVM may transmit a response back tothe client device 128 and/or the first node 130 over the network 126,such as a success message or an error message. In this way, theapplication server tier 106 may host SVMs, services, and/or otherstorage applications using the server (1) 116, the server (N) 118, etc.

A user interface tier 104 of the distributed computing platform 102 mayprovide the client device 128 and/or the first node 130 with access touser interfaces associated with the storage and access of data and/orother services provided by the distributed computing platform 102. In anexample, a service user interface 110 may be accessible from thedistributed computing platform 102 for accessing services subscribed toby clients and/or nodes, such as data replication services, applicationhosting services, data security services, human resource services,warehouse tracking services, accounting services, etc. For example,client user interfaces may be provided to corresponding clients, such asa client (1) user interface 112, a client (N) user interface 114, etc.The client (1) can access various services and resources subscribed toby the client (1) through the client (1) user interface 112, such asaccess to a web service, a development environment, a human resourceapplication, a warehouse tracking application, and/or other services andresources provided by the application server tier 106, which may usedata stored within the data storage tier 108.

The client device 128 and/or the first node 130 may subscribe to certaintypes and amounts of services and resources provided by the distributedcomputing platform 102. For example, the client device 128 may establisha subscription to have access to three virtual machines, a certainamount of storage, a certain type/amount of data redundancy, a certaintype/amount of data security, certain service level agreements (SLAs)and service level objectives (SLOs), latency guarantees, bandwidthguarantees, access to execute or host certain applications, etc.Similarly, the first node 130 can establish a subscription to haveaccess to certain services and resources of the distributed computingplatform 102.

As shown, a variety of clients, such as the client device 128 and thefirst node 130, incorporating and/or incorporated into a variety ofcomputing devices may communicate with the distributed computingplatform 102 through one or more networks, such as the network 126. Forexample, a client may incorporate and/or be incorporated into a clientapplication (e.g., software) implemented at least in part by one or moreof the computing devices.

Examples of suitable computing devices include personal computers,server computers, desktop computers, nodes, storage servers, nodes,laptop computers, notebook computers, tablet computers or personaldigital assistants (PDAs), smart phones, cell phones, and consumerelectronic devices incorporating one or more computing devicecomponents, such as one or more electronic processors, microprocessors,central processing units (CPU), or controllers. Examples of suitablenetworks include networks utilizing wired and/or wireless communicationtechnologies and networks operating in accordance with any suitablenetworking and/or communication protocol (e.g., the Internet). In usecases involving the delivery of customer support services, the computingdevices noted represent the endpoint of the customer support deliveryprocess, i.e., the consumer's device.

The distributed computing platform 102, such as a multi-tenant businessdata processing platform or cloud computing environment, may includemultiple processing tiers, including the user interface tier 104, theapplication server tier 106, and a data storage tier 108. The userinterface tier 104 may maintain multiple user interfaces, includinggraphical user interfaces and/or web-based interfaces. The userinterfaces may include the service user interface 110 for a service toprovide access to applications and data for a client (e.g., a “tenant”)of the service, as well as one or more user interfaces that have beenspecialized/customized in accordance with user specific requirements(e.g., as discussed above), which may be accessed via one or more APIs.

The service user interface 110 may include components enabling a tenantto administer the tenant's participation in the functions andcapabilities provided by the distributed computing platform 102, such asaccessing data, causing execution of specific data processingoperations, etc. Each processing tier may be implemented with a set ofcomputers, virtualized computing environments such as a storage virtualmachine or storage virtual server, and/or computer components includingcomputer servers and processors, and may perform various functions,methods, processes, or operations as determined by the execution of asoftware application or set of instructions.

The data storage tier 108 may include one or more data stores, which mayinclude the service data store 120 and one or more client data stores122-124. Each client data store may contain tenant-specific data that isused as part of providing a range of tenant-specific business andstorage services or functions, including but not limited to ERP, CRM,eCommerce, Human Resources management, payroll, storage services, etc.Data stores may be implemented with any suitable data storagetechnology, including structured query language (SQL) based relationaldatabase management systems (RDBMS), file systems hosted by operatingsystems, object storage, etc.

In accordance with one embodiment of the invention, the distributedcomputing platform 102 may be a multi-tenant and service platformoperated by an entity in order to provide multiple tenants with a set ofbusiness related applications, data storage, and functionality. Theseapplications and functionality may include ones that a business uses tomanage various aspects of its operations. For example, the applicationsand functionality may include providing web-based access to businessinformation systems, thereby allowing a user with a browser and anInternet or intranet connection to view, enter, process, or modifycertain types of business information or any other type of information.

A clustered network environment 200 that may implement one or moreaspects of the techniques described and illustrated herein is shown inFIG. 2. The clustered network environment 200 includes data storageapparatuses 202(1)-202(n) that are coupled over a cluster or clusterfabric 204 that includes one or more communication network(s) andfacilitates communication between the data storage apparatuses202(1)-202(n) (and one or more modules, components, etc. therein, suchas, node computing devices 206(1)-206(n), for example), although anynumber of other elements or components can also be included in theclustered network environment 200 in other examples. This technologyprovides a number of advantages including methods, non-transitorycomputer readable media, and computing devices that implement thetechniques described herein.

In this example, node computing devices 206(1)-206(n) can be primary orlocal storage controllers or secondary or remote storage controllersthat provide client devices 208(1)-208(n) with access to data storedwithin data storage devices 210(1)-210(n) and cloud storage device(s)236 (also referred to as cloud storage node(s)). The node computingdevices 206(1)-206(n) may be implemented as hardware, software (e.g., astorage virtual machine), or combination thereof.

The data storage apparatuses 202(1)-202(n) and/or node computing devices206(1)-206(n) of the examples described and illustrated herein are notlimited to any particular geographic areas and can be clustered locallyand/or remotely via a cloud network, or not clustered in other examples.Thus, in one example the data storage apparatuses 202(1)-202(n) and/ornode computing device 206(1)-206(n) can be distributed over a pluralityof storage systems located in a plurality of geographic locations (e.g.,located on-premise, located within a cloud computing environment, etc.);while in another example a clustered network can include data storageapparatuses 202(1)-202(n) and/or node computing device 206(1)-206(n)residing in a same geographic location (e.g., in a single on-site rack).

In the illustrated example, one or more of the client devices208(1)-208(n), which may be, for example, personal computers (PCs),computing devices used for storage (e.g., storage servers), or othercomputers or peripheral devices, are coupled to the respective datastorage apparatuses 202(1)-202(n) by network connections 212(1)-212(n).Network connections 212(1)-212(n) may include a local area network (LAN)or wide area network (WAN) (i.e., a cloud network), for example, thatutilize TCP/IP and/or one or more Network Attached Storage (NAS)protocols, such as a Common Internet Filesystem (CIFS) protocol or aNetwork Filesystem (NFS) protocol to exchange data packets, a StorageArea Network (SAN) protocol, such as Small Computer System Interface(SCSI) or Fiber Channel Protocol (FCP), an object protocol, such assimple storage service (S3), and/or non-volatile memory express (NVMe),for example.

Illustratively, the client devices 208(1)-208(n) may be general-purposecomputers running applications and may interact with the data storageapparatuses 202(1)-202(n) using a client/server model for exchange ofinformation. That is, the client devices 208(1)-208(n) may request datafrom the data storage apparatuses 202(1)-202(n) (e.g., data on one ofthe data storage devices 210(1)-210(n) managed by a network storagecontroller configured to process I/O commands issued by the clientdevices 208(1)-208(n)), and the data storage apparatuses 202(1)-202(n)may return results of the request to the client devices 208(1)-208(n)via the network connections 212(1)-212(n).

The node computing devices 206(1)-206(n) of the data storage apparatuses202(1)-202(n) can include network or host nodes that are interconnectedas a cluster to provide data storage and management services, such as toan enterprise having remote locations, cloud storage (e.g., a storageendpoint may be stored within cloud storage device(s) 236), etc., forexample. Such node computing devices 206(1)-206(n) can be attached tothe cluster fabric 204 at a connection point, redistribution point, orcommunication endpoint, for example. One or more of the node computingdevices 206(1)-206(n) may be capable of sending, receiving, and/orforwarding information over a network communications channel, and couldcomprise any type of device that meets any or all of these criteria.

In an example, the node computing devices 206(1) and 206(n) may beconfigured according to a disaster recovery configuration whereby asurviving node provides switchover access to the storage devices210(1)-210(n) in the event a disaster occurs at a disaster storage site(e.g., the node computing device 206(1) provides client device 212(n)with switchover data access to data storage devices 210(n) in the eventa disaster occurs at the second storage site). In other examples, thenode computing device 206(n) can be configured according to an archivalconfiguration and/or the node computing devices 206(1)-206(n) can beconfigured based on another type of replication arrangement (e.g., tofacilitate load sharing). Additionally, while two node computing devicesare illustrated in FIG. 2, any number of node computing devices or datastorage apparatuses can be included in other examples in other types ofconfigurations or arrangements.

As illustrated in the clustered network environment 200, node computingdevices 206(1)-206(n) can include various functional components thatcoordinate to provide a distributed storage architecture. For example,the node computing devices 206(1)-206(n) can include network modules214(1)-214(n) and disk modules 216(1)-216(n). Network modules214(1)-214(n) can be configured to allow the node computing devices206(1)-206(n) (e.g., network storage controllers) to connect with clientdevices 208(1)-208(n) over the storage network connections212(1)-212(n), for example, allowing the client devices 208(1)-208(n) toaccess data stored in the clustered network environment 200.

Further, the network modules 214(1)-214(n) can provide connections withone or more other components through the cluster fabric 204. Forexample, the network module 214(1) of node computing device 206(1) canaccess the data storage device 210(n) by sending a request via thecluster fabric 204 through the disk module 216(n) of node computingdevice 206(n) when the node computing device 206(n) is available.Alternatively, when the node computing device 206(n) fails, the networkmodule 214(1) of node computing device 106(1) can access the datastorage device 210(n) directly via the cluster fabric 204. The clusterfabric 204 can include one or more local and/or wide area computingnetworks (i.e., cloud networks) embodied as Infiniband, Fibre Channel(FC), or Ethernet networks, for example, although other types ofnetworks supporting other protocols can also be used.

Disk modules 216(1)-216(n) can be configured to connect data storagedevices 210(1)-210(n), such as disks or arrays of disks, SSDs, flashmemory, or some other form of data storage, to the node computingdevices 206(1)-206(n). Often, disk modules 216(1)-216(n) communicatewith the data storage devices 210(1)-210(n) according to the SANprotocol, such as SCSI or FCP, for example, although other protocols canalso be used. Thus, as seen from an operating system on node computingdevices 206(1)-206(n), the data storage devices 210(1)-210(n) can appearas locally attached. In this manner, different node computing devices206(1)-206(n), etc. may access data blocks, files, or objects throughthe operating system, rather than expressly requesting abstract files.

While the clustered network environment 200 illustrates an equal numberof network modules 214(1)-214(n) and disk modules 216(1)-216(n), otherexamples may include a differing number of these modules. For example,there may be a plurality of network and disk modules interconnected in acluster that do not have a one-to-one correspondence between the networkand disk modules. That is, different node computing devices can have adifferent number of network and disk modules, and the same nodecomputing device can have a different number of network modules thandisk modules.

Further, one or more of the client devices 208(1)-208(n) can benetworked with the node computing devices 206(1)-206(n) in the cluster,over the storage connections 212(1)-212(n). As an example, respectiveclient devices 208(1)-208(n) that are networked to a cluster may requestservices (e.g., exchanging of information in the form of data packets)of node computing devices 206(1)-206(n) in the cluster, and the nodecomputing devices 206(1)-206(n) can return results of the requestedservices to the client devices 208(1)-208(n). In one example, the clientdevices 208(1)-208(n) can exchange information with the network modules214(1)-214(n) residing in the node computing devices 206(1)-206(n)(e.g., network hosts) in the data storage apparatuses 202(1)-202(n).

In one example, the storage apparatuses 202(1)-202(n) host aggregatescorresponding to physical local and remote data storage devices, such aslocal flash or disk storage in the data storage devices 210(1)-210(n),for example. One or more of the data storage devices 210(1)-210(n) caninclude mass storage devices, such as disks of a disk array. The disksmay comprise any type of mass storage devices, including but not limitedto magnetic disk drives, flash memory, and any other similar mediaadapted to store information, including, for example, data and/or parityinformation.

The aggregates include volumes 218(1)-218(n) in this example, althoughany number of volumes can be included in the aggregates. The volumes218(1)-218(n) are virtual data stores or storage objects that define anarrangement of storage and one or more filesystems within the clusterednetwork environment 200. Volumes 218(1)-218(n) can span a portion of adisk or other storage device, a collection of disks, or portions ofdisks, for example, and typically define an overall logical arrangementof data storage. In one example volumes 218(1)-218(n) can include storeduser data as one or more files, blocks, or objects that may reside in ahierarchical directory structure within the volumes 218(1)-218(n).

Volumes 218(1)-218(n) are typically configured in formats that may beassociated with particular storage systems, and respective volumeformats typically comprise features that provide functionality to thevolumes 218(1)-218(n), such as providing the ability for volumes218(1)-218(n) to form clusters, among other functionality. Optionally,one or more of the volumes 218(1)-218(n) can be in composite aggregatesand can extend between one or more of the data storage devices210(1)-210(n) and one or more of the cloud storage device(s) 236 toprovide tiered storage, for example, and other arrangements can also beused in other examples.

In one example, to facilitate access to data stored on the disks orother structures of the data storage devices 210(1)-210(n), a filesystemmay be implemented that logically organizes the information as ahierarchical structure of directories and files. In this example,respective files may be implemented as a set of disk blocks of aparticular size that are configured to store information, whereasdirectories may be implemented as specially formatted files in whichinformation about other files and directories are stored.

Data can be stored as files or objects within a physical volume and/or avirtual volume, which can be associated with respective volumeidentifiers. The physical volumes correspond to at least a portion ofphysical storage devices, such as the data storage devices 210(1)-210(n)(e.g., a Redundant Array of Independent (or Inexpensive) Disks (RAIDsystem)) whose address, addressable space, location, etc. does notchange. Typically the location of the physical volumes does not changein that the range of addresses used to access it generally remainsconstant.

Virtual volumes, in contrast, can be stored over an aggregate ofdisparate portions of different physical storage devices. Virtualvolumes may be a collection of different available portions of differentphysical storage device locations, such as some available space fromdisks, for example. It will be appreciated that since the virtualvolumes are not “tied” to any one particular storage device, virtualvolumes can be said to include a layer of abstraction or virtualization,which allows it to be resized and/or flexible in some regards.

Further, virtual volumes can include one or more logical unit numbers(LUNs), directories, Qtrees, files, and/or other storage objects, forexample. Among other things, these features, but more particularly theLUNs, allow the disparate memory locations within which data is storedto be identified, for example, and grouped as data storage unit. Assuch, the LUNs may be characterized as constituting a virtual disk ordrive upon which data within the virtual volumes is stored within anaggregate. For example, LUNs are often referred to as virtual drives,such that they emulate a hard drive, while they actually comprise datablocks stored in various parts of a volume.

In one example, the data storage devices 210(1)-210(n) can have one ormore physical ports, wherein each physical port can be assigned a targetaddress (e.g., SCSI target address). To represent respective volumes, atarget address on the data storage devices 210(1)-210(n) can be used toidentify one or more of the LUNs. Thus, for example, when one of thenode computing devices 206(1)-206(n) connects to a volume, a connectionbetween the one of the node computing devices 206(1)-206(n) and one ormore of the LUNs underlying the volume is created.

Respective target addresses can identify multiple of the LUNs, such thata target address can represent multiple volumes. The I/O interface,which can be implemented as circuitry and/or software in a storageadapter or as executable code residing in memory and executed by aprocessor, for example, can connect to volumes by using one or moreaddresses that identify the one or more of the LUNs.

Referring to FIG. 3, node computing device 206(1) in this particularexample includes processor(s) 300, a memory 302, a network adapter 304,a cluster access adapter 306, and a storage adapter 308 interconnectedby a system bus 310. In other examples, the node computing device 206(1)comprises a virtual machine, such as a virtual storage machine. The nodecomputing device 206(1) also includes a storage operating system 312installed in the memory 302 that can, for example, implement a RAID dataloss protection and recovery scheme to optimize reconstruction of dataof a failed disk or drive in an array, along with other functionalitysuch as deduplication, compression, snapshot creation, data mirroring,synchronous replication, asynchronous replication, encryption, etc. Insome examples, the node computing device 206(n) is substantially thesame in structure and/or operation as node computing device 206(1),although the node computing device 206(n) can also include a differentstructure and/or operation in one or more aspects than the nodecomputing device 206(1).

The network adapter 304 in this example includes the mechanical,electrical and signaling circuitry needed to connect the node computingdevice 206(1) to one or more of the client devices 208(1)-208(n) overnetwork connections 212(1)-212(n), which may comprise, among otherthings, a point-to-point connection or a shared medium, such as a localarea network. In some examples, the network adapter 304 furthercommunicates (e.g., using TCP/IP) via the cluster fabric 204 and/oranother network (e.g. a WAN) (not shown) with cloud storage device(s)236 to process storage operations associated with data stored thereon.

The storage adapter 308 cooperates with the storage operating system 312executing on the node computing device 206(1) to access informationrequested by one of the client devices 208(1)-208(n) (e.g., to accessdata on a data storage device 210(1)-210(n) managed by a network storagecontroller). The information may be stored on any type of attached arrayof writeable media such as magnetic disk drives, flash memory, and/orany other similar media adapted to store information.

In the exemplary data storage devices 210(1)-210(n), information can bestored in data blocks on disks. The storage adapter 308 can include I/Ointerface circuitry that couples to the disks over an I/O interconnectarrangement, such as a storage area network (SAN) protocol (e.g., SmallComputer System Interface (SCSI), Internet SCSI (iSCSI), hyperSCSI,Fiber Channel Protocol (FCP)). The information is retrieved by thestorage adapter 308 and, if necessary, processed by the processor(s) 300(or the storage adapter 308 itself) prior to being forwarded over thesystem bus 310 to the network adapter 304 (and/or the cluster accessadapter 306 if sending to another node computing device in the cluster)where the information is formatted into a data packet and returned to arequesting one of the client devices 208(1)-208(2) and/or sent toanother node computing device attached via the cluster fabric 204. Insome examples, a storage driver 314 in the memory 302 interfaces withthe storage adapter to facilitate interactions with the data storagedevices 210(1)-210(n).

The storage operating system 312 can also manage communications for thenode computing device 206(1) among other devices that may be in aclustered network, such as attached to a cluster fabric 204. Thus, thenode computing device 206(1) can respond to client device requests tomanage data on one of the data storage devices 210(1)-210(n) or cloudstorage device(s) 236 (e.g., or additional clustered devices) inaccordance with the client device requests.

The file system module 318 of the storage operating system 312 canestablish and manage one or more filesystems including software code anddata structures that implement a persistent hierarchical namespace offiles and directories, for example. As an example, when a new datastorage device (not shown) is added to a clustered network system, thefile system module 318 is informed where, in an existing directory tree,new files associated with the new data storage device are to be stored.This is often referred to as “mounting” a filesystem.

In the example node computing device 206(1), memory 302 can includestorage locations that are addressable by the processor(s) 300 andadapters 304, 306, and 308 for storing related software application codeand data structures. The processor(s) 300 and adapters 304, 306, and 308may, for example, include processing elements and/or logic circuitryconfigured to execute the software code and manipulate the datastructures.

The storage operating system 312, portions of which are typicallyresident in the memory 302 and executed by the processor(s) 300, invokesstorage operations in support of a file service implemented by the nodecomputing device 206(1). Other processing and memory mechanisms,including various computer readable media, may be used for storingand/or executing application instructions pertaining to the techniquesdescribed and illustrated herein. For example, the storage operatingsystem 312 can also utilize one or more control files (not shown) to aidin the provisioning of virtual machines.

In this particular example, the memory 302 also includes a moduleconfigured to implement the techniques described herein, including forexample maintaining timestamp parity as discussed above and furtherbelow.

The examples of the technology described and illustrated herein may beembodied as one or more non-transitory computer or machine readablemedia, such as the memory 302, having machine or processor-executableinstructions stored thereon for one or more aspects of the presenttechnology, which when executed by processor(s), such as processor(s)300, cause the processor(s) to carry out the steps necessary toimplement the methods of this technology, as described and illustratedwith the examples herein. In some examples, the executable instructionsare configured to perform one or more steps of a method described andillustrated later.

One embodiment of maintaining timestamp parity during a transitionreplay phase to a synchronous state by utilizing an indicator isillustrated by an exemplary method 400 of FIG. 4 and further describedin conjunction with system 500 of FIGS. 5A-5G.

A primary node 502 (e.g., a computing device, a server, a virtualmachine, hardware, software, cloud computing resources, or anycombination thereof) may maintain one or more basefiles which also haveadditional data streams, and the basefile and the additional datastreams are accessed by a client using the CIFS protocol, as illustratedby FIG. 5A. For example, the primary node 502 may maintain a basefile506 within which client devices may store and access content through afirst stream 512, a second stream 514, and/or any other number ofstreams (depicted using “stream(N)” in FIGS. 5A-5G). In an embodiment,the basefile 506 represents main content of a CIFS file associated withone or more data streams. The basefile 506 may be associated with amodify timestamp 508. The modify timestamp 508 may correspond to a lasttime at which the basefile 506 was written to by an operation (e.g., awrite data operation to a stream associated with the basefile 506). Thebasefile 506 may be associated with a change timestamp 510. The changetimestamp 510 may correspond to a time at which an inode of the basefile506 was modified by an operation (e.g., a write data operation to astream associated with the basefile 506, a close stream operation toclose the stream associated with the basefile 506, etc.). Initially, thechange timestamp 510 may have a value of t0 and the modify timestamp 508may have a value of t0, for example.

A secondary node 504 may maintain a replicated basefile 522 that is areplica of the basefile 506. The replicated basefile 522 is associatedwith a modify timestamp 526 and a change timestamp 524. In order tomaintain consistency between the basefile 506 and the replicatedbasefile 522, the modify timestamp 526 of the replicated basefile 522should have the same value as the modify timestamp 508 of the basefile506 and the change timestamp 524 of the replicated basefile 522 shouldhave the same value as the change timestamp 510 of the basefile 506. Inthis way, if the primary node 502 fails and the secondary node 504 takesover for the failed primary node 502, the secondary node 504 can provideclients, application, and services with access to the same data andmetadata such as timestamp metadata as what was previously accessiblethrough the primary node 502 before the failure.

The primary node 502 and the secondary node 504 may be out-of-sync, suchas in a non-synchronous replication state (e.g., an asynchronousreplication state, a state where no replication is being performed, asemi-synchronous state, etc.), such that operations targeting thebasefile 506 and/or streams of the basefile 506 are not beingsynchronously replicated to the basefile 522 before being acknowledgedback as being completed. Accordingly, a transition logging phase and atransition replay phase may be performed to bring the primary node 502and the secondary node 504 into an in-sync state such as a synchronousreplication state.

During the transition logging phase, a dirty region log 520 is used totrack regions within a storage object (e.g., regions within a volumewithin which the basefile 506 is stored) that are modified by dataoperations executed by the primary node 502. The dirty region log 520may comprise indicators (e.g., bits in examples discussed herein) thatcan be set to either a dirty indicator or a clean indicator. A bit maybe mapped to a region within the storage object (e.g., a bit maycorrespond to the basefile 506, a portion of the basefile 506, or thebasefile 506 along with other basefiles). Thus, the bit can be set tothe dirty indicator to indicate that a data operation has modified theregion (e.g., the region now comprises data not yet replicated to areplicated storage object, such as a region of a volume within which thereplicated basefile 522 is stored by the secondary node 504). The bitcan be set to the clean indicator to indicate that the region is clean(e.g., the region does not comprise data not yet replicated to thereplicated storage object, and thus the region comprises the same dataas a corresponding region within the replicated storage object, such aswhere the basefile 506 and the replicated basefile 522 comprise the samedata). In an embodiment of utilizing the dirty region log 520 to trackdirty data, incore splitter objects are set to a dirty region loggingstate to ensure incoming write operations are intercepted by thesplitter objects, and for each region that is modified by the incomingwrite operations, dirty bits are set in the dirty region log 520.

In an embodiment as illustrated by FIG. 5B, the primary node 502 mayreceive a write data operation 528 targeting the first stream 512 of thebasefile 506. The write data operation 528 may be executed by theprimary node 502 upon the first stream 512 of the basefile 506,resulting in dirty data within the basefile 506. Accordingly, an entryis created within the dirty region log 520 to indicate that a regionwithin which the basefile 506 is stored has been modified with data notyet replicated to a corresponding region within which the replicatedbasefile 522 is stored. That is, the entry indicates that the write dataoperation 528 modified the first stream 512 of the basefile 506 at theregion of storage that is now considered dirty. The primary node 502 mayexecute the write data operation 528 at first time t1. Thus, the valueof the modify timestamp 508 is changed from t0 to the first time t1, andthe value of the change timestamp 510 is changed from t0 to the firsttime t1, all in response to the write data operation 528.

During the transition logging phase, a metadata log 518 is also used totrack metadata operations executed by the primary node 502, such as acreate operation (e.g., a create file operation, a create LUN operation,a create stream operation, a create basefile operation, etc.), a linkoperation, an unlink operation, a rename operation (e.g., a file renameoperation, etc.), a set attribute operation (e.g., a set volume sizeoperation, an assign permissions operation, etc.), a close operation(e.g., a close stream operation that closes and deletes a streamassociated with a basefile), etc. For example, a metadata operation anda change timestamp modified by execution of the metadata operation maybe logged into the metadata log 518.

In an embodiment as illustrated by FIG. 5C, the primary node 502receives a metadata operation, such as a close stream operation 530 toclose and delete 532 the first stream 512 of the basefile 506. At 402(of FIG. 4's exemplary method 400), the close stream operation 530 maybe identified as a metadata operation that closes the first stream 512associated with the basefile 506 of the primary node 502 that could havebeen modified by one or more previously executed write data operations.The execution of the close stream operation 530 may be performed at asecond time t2 subsequent the first time t1 at which the write dataoperation 528 was executed by the primary node 502. Because execution ofthe close stream operation 530 affects the change timestamp 510 but doesnot change the modify timestamp 508 of the basefile 506, the changetimestamp 510 is set to the second time t2 and the modify timestamp 508remains at the first time t1. Accordingly, the close stream operation530 and the change timestamp 510 of the second time t2 are recorded intothe metadata log 518.

At 404, a determination is made as to whether the dirty region log 520comprises an entry for the first stream 512 indicating that a write dataoperation modified the first stream 512 before the close streamoperation 530 closed and deleted 532 the first stream 512. Thedetermination will result in the identification of the entry for thefirst stream 512 indicating that the write data operation 528 modifiedthe first stream 512 before the close stream operation 530 is executed.

At 406, in response to identifying the entry within the dirty region log520 for the first stream 512 modified by the write data operation 528,an indicator 531, such as a flag, a bit, or any other type of indicator,is set to specify that the first stream 512 was deleted 532 by the closestream operation 530 (e.g., an indicator may be created, an existingindicator may be set to a first value such as 1, etc.). The indicator531 may be stored within the dirty region log 520 or may be storedseparate from the dirty region log 520. In an embodiment, the indicator531 may comprise a flag indicating that the first stream 512 is an NTstream associated with a CIFS file. If the dirty region log 520 did notcomprise any entries for the first stream 512, then the indicator 531may not be created or an existing indicator may not be set to specifythat the first stream 512 was deleted 532 by the close stream operation530 (e.g., no indicator may be created, the indicator may be set to orretained at a second value such as 0, etc.).

As illustrated by FIG. 5D, a transition replay phase 540 may beperformed, at 408 (FIG. 4) to transition the primary node 502 and thesecondary node 504 from the asynchronous replication state to thein-sync state. During the transition replay phase 540, metadataoperations logged within the metadata log 518 are replayed to thesecondary node 504 during a metadata log replay phase 542 of thetransition replay phase 540. In particular, a cutover scanner may beimplemented at the primary node 502 or at any other device or node. Thecutover scanner may extract metadata operations from the metadata log518 and transmit the extracted metadata operations to the secondary node504 for execution at the secondary node 604. The metadata operations maybe extracted, transmitted, and/or executed in an order corresponding tosequence numbers assigned to the metadata operations by the primary node502 (e.g., sequence numbers are assigned according to an order at whichthe primary node 502 executed the metadata operations). In this way, thesecondary node 504 executes the metadata operations in a same order thatthe metadata operations were previously executed by the primary node502. In an example of replaying the metadata operations, the closestream operation 530 is replayed at the secondary node 504, whichresults in the change timestamp 524 of the replicated basefile 522 beingset to the second time t2 at which the close stream operation 530 wasexecuted by the primary node 502. However, the modify timestamp 526 ofthe replicated basefile 522 remains at t0 since the close streamoperation 530 did not change the modify timestamp 508 of the basefile506 when executed by the primary node 502.

In an embodiment, metadata operations are replayed before dirty data(e.g., data within regions of storage maintained by the primary node 502having dirty indicators within the dirty region log 520) is replicatedto the secondary node 504. Accordingly, once the metadata log replayphase 542 has completed, a dirty region log scan phase 550 of thetransition replay phase 540 is performed to replicate data of theregions tracked within the dirty region log 520 as being dirty (e.g.,comprising data not yet replicated to the secondary node 504). When adirty indicator for a region is encountered within the dirty region log520, data within that region is replicated from the primary node 502 toa corresponding region of storage maintained by the secondary node 504.When a clean indicator for a region is encountered within the dirtyregion log 520, that region is skipped and no replication is performedbecause the data within that region is already stored within acorresponding region of storage maintained by the secondary node 504.

As illustrated by FIG. 5F, during the dirty region log scan phase 550,the indicator 531 may be encountered (e.g., the indicator 531 may beencountered within the dirty region log 520 or may be maintainedseparate from the dirty region log 520, but is also evaluated by thedirty region log scan phase 550). Upon encountering the indicator 531for the first stream 512, the dirty region log scan is failed 551 tocause a resynchronization 560 to be performed, as illustrated by FIG.5G. During the resynchronization 560, one or more asynchronous transfers(e.g., asynchronous incremental transfers) are performed to replicateinformation (e.g., data, metadata, timestamps, etc.) from the primarynode 502 to the secondary node 504. Accordingly, the modify timestamp508 of the first time t1 for the basefile 506 maintained by the primarynode 502 will be replicated to the secondary node 504 and applied to themodify timestamp 526 for the replicated basefile 522 to change the valueof the replicated basefile 522 from t0 to the first time t1. In anembodiment, the modify timestamp 508 and the change timestamp 510 of abase inode associated with the basefile 506 are replicated to thesecondary node 504.

In this way, the indicator 531 is a trigger that results in theidentification and replication of data and/or metadata from the primarynode 502 to the secondary node 504, such as the modify timestamp 508 ofthe basefile 506 associated with the first stream 512 identified byindicator 531. In this way, timestamp parity/consistency between thebasefile 506 and the replicated basefile 522 is achieved. Because thecombination of back to back write data operations and close streamoperations to delete streams is not frequent, the resynchronization 560should successfully complete without encountering further issues. In anembodiment, the dirty region log scan phase 550 (FIG. 5E) may berestarted or resumed after the resynchronization 560 in order tocomplete the transition replay phase to transition the primary node 502and the secondary node 504 from being in the asynchronous replicationstate to being in the in-sync state.

One embodiment of maintaining timestamp parity during a transitionreplay phase to a synchronous state by logging modify timestamps into ametadata log is illustrated by an exemplary method 600 of FIG. 6 andfurther described in conjunction with system 700 of FIGS. 7A-7B.

A primary node 702 (e.g., a computing device, a server, a virtualmachine, hardware, software, cloud computing resources, or anycombination thereof) may maintain one or more basefiles that may beaccessed by client devices using streams (e.g., an NT stream), asillustrated by FIG. 7A. For example, the primary node 702 may maintain abasefile 706 within which client devices may store and access contentthrough a first stream 712 and/or other streams. In an embodiment, thebasefile 706 represents main content of a CIFS file associated with oneor more data streams. The basefile 706 may be associated with a modifytimestamp 708 corresponding to a last time at which the basefile 706 waswritten to by an operation. The basefile 706 may be associated with achange timestamp 710 corresponding to a time at which an inode of thebasefile 706 was last modified by an operation.

A secondary node 704 may maintain a replicated basefile 722 that is areplica of the basefile 706. The replicated basefile 722 is associatedwith a modify timestamp 726 and a change timestamp 724. Initially, themodify timestamp 726 and the change timestamp 724 of the replicatedbasefile 722 have a value of t0. In order to maintain consistencybetween the basefile 706 and the replicated basefile 722, the modifytimestamp 726 of the replicated basefile 722 should have the same valueas the modify timestamp 708 of the basefile 706 and the change timestamp724 of the replicated basefile 722 should have the same value as thechange timestamp 710 of the basefile 706. Thus, if the primary node 702fails and the secondary node 704 takes over for the failed primary node702, the secondary node 704 can provide clients, application, andservices with access to the same data and metadata such as timestampdata as what was previously accessible through the primary node 702before the failure of the primary node 702.

The primary node 702 and the secondary node 704 may be out-of-sync, suchas in a non-synchronous replication state (e.g., an asynchronousreplication state), such that operations targeting the basefile 706and/or streams of the basefile 706 are not being synchronouslyreplicated to the replicated basefile 722 before being acknowledged backas being completed. Accordingly, a transition logging phase and atransition replay phase may be performed to bring the primary node 702and the secondary node 704 into an in-sync state such as a synchronousreplication state.

During the transition logging phase, a dirty region log 714 is used totrack regions modified by data operations executed by the primary node702. For example, a write data operation may target the first stream 712of the basefile 706, such as to write to the first stream 712 which isassociated with the basefile 706. Accordingly, a region modified by thewrite data operation is tracked within the dirty region log 714 using adirty indicator to indicate that the region was modified with data notyet replicated to a corresponding region within storage of the secondarynode 704. The write data operation may be executed by the primary node702 upon the first stream 712 at a first time t1. Accordingly, themodify timestamp 708 and the change timestamp 710 of the basefile 706are set to the first time t1.

During the transition logging phase, a metadata log 716 is used to trackmetadata operations executed by the primary node 702, such as a createoperation (e.g., a create file operation, a create LUN operation, acreate stream operation, a create basefile operation, etc.), a linkoperation, an unlink operation, a rename operation (e.g., a file renameoperation, etc.), a set attribute operation (e.g., a set volume sizeoperation, an assign permissions operation, etc.), a close operation(e.g., a close stream operation that closes and deletes a streamassociated with a basefile), etc. In an embodiment, the primary node 702receives a close stream operation 718 during the transition loggingphase. At 602 (FIG. 6), the close stream operation 718 is identified asa metadata operation to close the first stream 712 associated with thebasefile 706 of the primary node 702. The close stream operation 718 isexecuted by the primary node 702 to close and delete 731 the firststream 712 of the basefile 706 at a second time t2. Accordingly, thechange timestamp 710 of the basefile 706 is set to the second time t2and the modify timestamp 708 remains at the first time t1.

At 604 (FIG. 6), the close stream operation 718 (FIG. 7A), the modifytimestamp 708 having the first time t1, and the change timestamp 710having the second time t2 are logged as entry 720 into the metadata log716. In an embodiment, the logging of the modify timestamp 708 istriggered based upon a determination that the first stream 712corresponds to an NT stream of a CIFS file that is deleted by the closestream operation 718. Without triggering the logging the modifytimestamp 708 having the first time t1, the modify timestamp 708 wouldnot normally be logged within the metadata log 716 because execution ofthe close stream operation 718 did not affect the modify timestamp 708.

At 606 (FIG. 6), a transition replay phase 703 (as illustrated by FIG.7B) is performed where metadata operations tracked within the metadatalog 716 are replayed to the secondary node 704 during a metadata logreplay phase 732 and data within regions (dirty regions) tracked withinthe dirty region log 714 are replicated to the secondary node 704 duringa dirty region log scan phase performed after the replay of the metadataoperations during the metadata log replay phase 732. The transitionreplay phase 703 is performed to bring the primary node 702 and thesecondary node 704 from an asynchronous replication state to an in-syncstate, such as a synchronous replication state.

During the metadata log replay phase 732 of the transition replay phase703, metadata operations logged within the metadata log 716 are replayedat the secondary node 704, along with modify timestamps and changetimestamps logged within the metadata log 716 being applied to thesecondary node 704. In an embodiment, the close stream operation 718,executed by the primary node 702 and logged within the metadata log 716,is replayed to the secondary node 704. The first time t1 of the modifytimestamp 708 logged within the metadata log 716 is applied to themodify timestamp 726 of the replicated basefile 722 so that the modifytimestamp 726 of the replicated basefile 722 is set to the first timet1. If the modify timestamp 708 was not logged within the metadata log716, then the modify timestamp 726 would not be set to the first time t1at the end of the transition replay phase 703 (e.g., at the end of thedirty region log scan phase because the close stream operation 718deleted the first stream 712). The second time t2 of the changetimestamp 710 logged within the metadata log 716 is applied to thechange timestamp 724 of the replicated basefile 722 so that the changetimestamp 724 of the replicated basefile 722 is set to the second timet2. In this way, timestamp parity/consistency is maintained between theprimary node 702 and the secondary node 704.

Still another embodiment involves a computer-readable medium 800comprising processor-executable instructions configured to implement oneor more of the techniques presented herein. An example embodiment of acomputer-readable medium or a computer-readable device that is devisedin these ways is illustrated in FIG. 8, wherein the implementationcomprises a computer-readable medium 808, such as a compactdisc-recordable (CD-R), a digital versatile disc-recordable (DVD-R),flash drive, a platter of a hard disk drive, etc., on which is encodedcomputer-readable data 806. This computer-readable data 806, such asbinary data comprising at least one of a zero or a one, in turncomprises processor-executable computer instructions 804 configured tooperate according to one or more of the principles set forth herein. Insome embodiments, the processor-executable computer instructions 804 areconfigured to perform a method 802, such as at least some of theexemplary method 400 of FIG. 4 and/or at least some of the exemplarymethod 600 of FIG. 6, for example. In some embodiments, theprocessor-executable computer instructions 804 are configured toimplement a system, such as at least some of the exemplary system 500 ofFIGS. 5A-5G and/or at least some of the exemplary system 700 of FIGS.7A-7B, for example. Many such computer-readable media are contemplatedto operate in accordance with the techniques presented herein.

In an embodiment, the described methods and/or their equivalents may beimplemented with computer executable instructions. Thus, in anembodiment, a non-transitory computer readable/storage medium isconfigured with stored computer executable instructions of analgorithm/executable application that when executed by a machine(s)cause the machine(s) (and/or associated components) to perform themethod. Example machines include but are not limited to a processor, acomputer, a server operating in a cloud computing system, a serverconfigured in a Software as a Service (SaaS) architecture, a smartphone, and so on. In an embodiment, a computing device is implementedwith one or more executable algorithms that are configured to performany of the disclosed methods.

It will be appreciated that processes, architectures and/or proceduresdescribed herein can be implemented in hardware, firmware and/orsoftware. It will also be appreciated that the provisions set forthherein may apply to any type of special-purpose computer (e.g., filehost, storage server and/or storage serving appliance) and/orgeneral-purpose computer, including a standalone computer or portionthereof, embodied as or including a storage system. Moreover, theteachings herein can be configured to a variety of storage systemarchitectures including, but not limited to, a network-attached storageenvironment and/or a storage area network and disk assembly directlyattached to a client or host computer. Storage system should thereforebe taken broadly to include such arrangements in addition to anysubsystems configured to perform a storage function and associated withother equipment or systems.

In some embodiments, methods described and/or illustrated in thisdisclosure may be realized in whole or in part on computer-readablemedia. Computer readable media can include processor-executableinstructions configured to implement one or more of the methodspresented herein, and may include any mechanism for storing this datathat can be thereafter read by a computer system. Examples of computerreadable media include (hard) drives (e.g., accessible via networkattached storage (NAS)), Storage Area Networks (SAN), volatile andnon-volatile memory, such as read-only memory (ROM), random-accessmemory (RAM), electrically erasable programmable read-only memory(EEPROM) and/or flash memory, compact disk read only memory (CD-ROM)s,CD-Rs, compact disk re-writeable (CD-RW)s, DVDs, cassettes, magnetictape, magnetic disk storage, optical or non-optical data storage devicesand/or any other medium which can be used to store data.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of implementing at least some of the claims.

Various operations of embodiments are provided herein. The order inwhich some or all of the operations are described should not beconstrued to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated given the benefit ofthis description. Further, it will be understood that not all operationsare necessarily present in each embodiment provided herein. Also, itwill be understood that not all operations are necessary in someembodiments.

Furthermore, the claimed subject matter is implemented as a method,apparatus, or article of manufacture using standard application orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer application accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

As used in this application, the terms “component”, “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentincludes a process running on a processor, a processor, an object, anexecutable, a thread of execution, an application, or a computer. By wayof illustration, both an application running on a controller and thecontroller can be a component. One or more components residing within aprocess or thread of execution and a component may be localized on onecomputer or distributed between two or more computers.

Moreover, “exemplary” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or”. In addition, “a” and “an” as used in thisapplication are generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Also, at least one of A and B and/or the like generally means A orB and/or both A and B. Furthermore, to the extent that “includes”,“having”, “has”, “with”, or variants thereof are used, such terms areintended to be inclusive in a manner similar to the term “comprising”.

Many modifications may be made to the instant disclosure withoutdeparting from the scope or spirit of the claimed subject matter. Unlessspecified otherwise, “first,” “second,” or the like are not intended toimply a temporal aspect, a spatial aspect, an ordering, etc. Rather,such terms are merely used as identifiers, names, etc. for features,elements, items, etc. For example, a first set of information and asecond set of information generally correspond to set of information Aand set of information B or two different or two identical sets ofinformation or the same set of information.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method comprising: during a transition loggingphase where metadata operations executed by a primary node are loggedinto a metadata log and regions modified by data operations executed bythe primary node are tracked within a dirty region log, identifying aclose stream operation that closes and deletes a stream associated witha basefile of the primary node; determining that the dirty region logcomprises an entry for the stream indicating that a write data operationmodified the stream before the close stream operation closed and deletedthe stream; setting an indicator to specify that the stream was deletedby the close stream operation in response to the entry indicating thatthe write data operation modified a change timestamp and a modifytimestamp and the close stream operation modified the change timestampand not the modify timestamp; in response to encountering, during atransition replay phase, the indicator specifying that the stream wasdeleted by the close stream operation, triggering a failure of thetransition replay phase and triggering a resynchronization to replicatethe modify timestamp.
 2. The method of claim 1, comprising: performingthe transition replay phase where the metadata operations tracked withinthe metadata log are replayed to a secondary node and data of theregions tracked within the dirty region log are replicated to thesecondary node to bring the primary node and secondary node from anasynchronous replication state to an in-sync state.
 3. The method ofclaim 2, wherein the metadata operations are replayed before the data ofthe regions tracked within the dirty region log are replicated to thesecondary node.
 4. The method of claim 2, comprising: performing a dirtyregion log scan phase of the transition replay phase where the data ofthe regions tracked within the dirty region log are replicated to thesecondary node; and in response to encountering the indicator specifyingthat the stream was deleted by the close stream operation, triggering afailure of the dirty region log scan of the dirty region log scan phaseand restarting the resynchronization.
 5. The method of claim 2,comprising: performing the resynchronization to replicate the modifytimestamp of the basefile from the primary node to a replicated basefilemaintained by the secondary node as a replica of the basefile inresponse to encountering the indicator for the stream.
 6. The method ofclaim 2, comprising: performing the resynchronization in response toencountering the indicator for the stream, wherein the resynchronizationperforms one or more asynchronous transfers to replicate the modifytimestamp and the change timestamp of a base inode associated with thebasefile from the primary node to the secondary node.
 7. The method ofclaim 1, wherein the write data operation, executed at a first time bythe primary node, sets the change timestamp and the modify timestamp ofthe basefile to the first time.
 8. The method of claim 7, wherein theclose stream operation, executed at a second time subsequent the firsttime by the primary node, results in the change timestamp being set tothe second time and the modify timestamp remaining at the first time. 9.The method of claim 7, wherein the modify timestamp corresponds to alast modified time of the basefile.
 10. The method of claim 7, whereinthe change timestamp corresponds to a time at which an inode of thebasefile is modified.
 11. The method of claim 1, comprising: in responseto the dirty region log comprising no entries corresponding to thestream, refraining from setting the indicator.
 12. The method of claim1, wherein the indicator comprises a flag indicating that the entrywithin the dirty region log is for the stream that was deleted.
 13. Anon-transitory machine readable medium comprising instructions forperforming a method, which when executed by a machine, causes themachine to: during a transition logging phase where metadata operationsexecuted by a primary node are logged into a metadata log and regionsmodified by data operations executed by the primary node are trackedwithin a dirty region log, identify a close stream operation that closesand deletes a stream associated with a basefile of the primary node;determine that the dirty region log comprises an entry for the streamindicating that a write data operation modified the stream before theclose stream operation closed and deleted the stream; set an indicatorto specify that the stream was deleted by the close stream operation inresponse to the entry indicating that the write data operation modifieda change timestamp and a modify timestamp and the close stream operationmodified the change timestamp and not the modify timestamp; in responseto encountering, during a transition replay phase, the indicatorspecifying that the stream was deleted by the close stream operation,trigger a failure of the transition replay phase and trigger aresynchronization to replicate the modify timestamp.
 14. Thenon-transitory machine readable medium of claim 13, wherein theinstructions cause the machine to: perform the transition replay phasewhere the metadata operations tracked within the metadata log arereplayed to a secondary node and data of the regions tracked within thedirty region log are replicated to the secondary node to bring theprimary node and secondary node from an asynchronous replication stateto an in-sync state.
 15. The non-transitory machine readable medium ofclaim 14, wherein the instructions cause the machine to: during ametadata log replay phase of the transition replay phase, replicate themodify timestamp of the basefile from the metadata log to the secondarynode to apply to a replicated basefile maintained as a replica of thebasefile.
 16. The non-transitory machine readable medium of claim 14,wherein the instructions cause the machine to: during a metadata logreplay phase of the transition replay phase, replicate the changetimestamp of the basefile from the metadata log to the secondary node toapply to a replicated basefile maintained as a replica of the basefile.17. The non-transitory machine readable medium of claim 14, wherein theinstructions cause the machine to: triggering the logging of the modifytimestamp and the change timestamp based upon a determination that theclose stream operation caused deletion of the stream of the basefile.18. A computing device comprising: a memory comprising machineexecutable code for performing a method; and a processor coupled to thememory, the processor configured to execute the machine executable codeto cause the processor to: during a transition logging phase wheremetadata operations executed by a primary node are logged into ametadata log and regions modified by data operations executed by theprimary node are tracked within a dirty region log, identify a closestream operation that closes and deletes a stream associated with abasefile of the primary node; determine that the dirty region logcomprises an entry for the stream indicating that a write data operationmodified the stream before the close stream operation closed and deletedthe stream; set an indicator to specify that the stream was deleted bythe close stream operation in response to the entry indicating that thewrite data operation modified a change timestamp and a modify timestampand the close stream operation modified the change timestamp and not themodify timestamp; in response to encountering, during a transitionreplay phase, the indicator specifying that the stream was deleted bythe close stream operation, trigger a failure of the transition replayphase and trigger a resynchronization to replicate the modify timestamp.19. The computing device of claim 18, wherein the metadata operationsare replayed before the data of the regions tracked within the dirtyregion log are replicated to a secondary node.
 20. The computing deviceof claim 18, wherein the modify timestamp corresponds to a last modifiedtime of the basefile.